Improving Ontology Recommendation and Reuse in WebCORE by Collaborative Assessments

In this work, we present an extension of CORE [8], a tool for Collaborative Ontology Reuse and Evaluation. The system receives an informal description of a specific semantic domain and determines which ontologies from a repository are the most appropriate to describe the given domain. For this task, the environment is divided into three modules. The first component receives the problem description as a set of terms, and allows the user to refine and enlarge it using WordNet. The second module applies multiple automatic criteria to evaluate the ontologies of the repository, and determines which ones fit best the problem description. A ranked list of ontologies is returned for each criterion, and the lists are combined by means of rank fusion techniques. Finally, the third component uses manual user evaluations in order to incorporate a human, collaborative assessment of the ontologies. The new version of the system incorporates several novelties, such as its implementation as a web application; the incorporation of a NLP module to manage the problem definitions; modifications on the automatic ontology retrieval strategies; and a collaborative framework to find potential relevant terms according to previous user queries. Finally, we present some early experiments on ontology retrieval and evaluation, showing the benefits of our system

Semantic disambiguation and contextualisation of social tags

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-28509-7_18This manuscript is an extended version of the paper ‘cTag: Semantic Contextualisation of Social Tags’, presented at the 6th International Workshop on Semantic Adaptive Social Web (SASWeb 2011).We present an algorithmic framework to accurately and efficiently identify the semantic meanings and contexts of social tags within a particular folksonomy. The framework is used for building contextualised tag-based user and item profiles. We also present its implementation in a system called cTag, with which we preliminary analyse semantic meanings and contexts of tags belonging to Delicious and MovieLens folksonomies. The analysis includes a comparison between semantic similarities obtained for pairs of tags in Delicious folksonomy, and their semantic distances in the whole Web, according to co-occurrence based metrics computed with results of a Web search engine.This work was supported by the Spanish Ministry of Science and Innovation (TIN2008-06566-C04-02), and Universidad Autónoma de Madrid (CCG10-UAM/TIC-5877

Neighbor Selection and Weighting in User-Based Collaborative Filtering: A Performance Prediction Approach

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on the Web, http://dx.doi.org/10.1145/2579993User-based collaborative filtering systems suggest interesting items to a user relying on similar-minded people called neighbors. The selection and weighting of these neighbors characterize the different recommendation approaches. While standard strategies perform a neighbor selection based on user similarities, trust-aware recommendation algorithms rely on other aspects indicative of user trust and reliability. In this article we restate the trust-aware recommendation problem, generalizing it in terms of performance prediction techniques, whose goal is to predict the performance of an information retrieval system in response to a particular query. We investigate how to adopt the preceding generalization to define a unified framework where we conduct an objective analysis of the effectiveness (predictive power) of neighbor scoring functions. The proposed framework enables discriminating whether recommendation performance improvements are caused by the used neighbor scoring functions or by the ways these functions are used in the recommendation computation. We evaluated our approach with several state-of-the-art and novel neighbor scoring functions on three publicly available datasets. By empirically comparing four neighbor quality metrics and thirteen performance predictors, we found strong predictive power for some of the predictors with respect to certain metrics. This result was then validated by checking the final performance of recommendation strategies where predictors are used for selecting and/or weighting user neighbors. As a result, we have found that, by measuring the predictive power of neighbor performance predictors, we are able to anticipate which predictors are going to perform better in neighbor-scoring-powered versions of a user-based collaborative filtering algorithm.This research was supported by the Spanish Ministry of Science and Research (TIN2011-28538-C02-01). Part of this work was carried out during the tenure of an ERCIM “Alain Bensoussan” Fellowship Programme, funded by European Comission FP7 grant agreement no. 246016

Aplicación del análisis dedrocronológico de Retama sphaerocarpa L. (Boiss) para datar el abandono agrícola

Abandonment of agricultural land leads to changes in soil characteristics that may result in better or worse soil conditions. These changes are slow therefore the use of indicators for dating the time of abandonment is particularly useful. This study was carried out in Madrid, Spain with the aim to establish for the first time the use of Retama sphaerocarpa L. (Boiss) as a dendrochronological tool for dating land abandonment. This offers the possibility to take into consideration a period of time long enough for changes in soil to be determined. Such changes can be indicated by fluctuations in soil organic carbon content (SOC), porosity or water availability. Three different situations resulted from the dendrochronological analysis: soil currently tilled; soil recently abandoned (less than 5 years), and prolonged abandonment (in average 10 years). In addition the influence of Retama sphaerocarpa L. (Boiss) on soils was checked for these periods of abandonment. The rate of SOC gain can be considered fast. Tilled soils accounted for 0.48% SOC, and reached 1% in less than 5 years, although with wide standard deviations. Due to prolonged abandonment SOC reached 1.41%, (P = 0.09). Total soil porosity under tillage was 49%, and decreased to 38% after 4-5 years, but recovered to 41% under prolonged abandonment. Water availability (volumetric soil moisture between field capacity and permanent wilting point) remained the same, ranging from 7.7 to 8.5% along the whole period of time. The presence of R. sphaerocarpa L. (Boiss) accelerates soil changes as SOC in prolonged abandonment increased to 2.65%, porosity was 41% and water availability 10.3

Soluble epoxide hydrolase inhibition to face neuroinflammation in Parkinson's disease: a new therapeutic strategy

Neuroinflammation is a crucial process associated with the pathogenesis of neurodegenerative diseases, including Parkinson's disease (PD). Several pieces of evidence suggest an active role of lipid mediators, especially epoxy-fatty acids (EpFAs), in the genesis and control of neuroinflammation; 14,15-epoxyeicosatrienoic acid (14,15-EET) is one of the most commonly studied EpFAs, with anti-inflammatory properties. Soluble epoxide hydrolase (sEH) is implicated in the hydrolysis of 14,15-EET to its corresponding diol, which lacks anti-inflammatory properties. Preventing EET degradation thus increases its concentration in the brain through sEH inhibition, which represents a novel pharmacological approach to foster the reduction of neuroinflammation and by end neurodegeneration. Recently, it has been shown that sEH levels increase in brains of PD patients. Moreover, the pharmacological inhibition of the hydrolase domain of the enzyme or the use of sEH knockout mice reduced the deleterious effect of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) administration. This paper overviews the knowledge of sEH and EETs in PD and the importance of blocking its hydrolytic activity, degrading EETs in PD physiopathology. We focus on imperative neuroinflammation participation in the neurodegenerative process in PD and the putative therapeutic role for sEH inhibitors. In this review, we also describe highlights in the general knowledge of the role of sEH in the central nervous system (CNS) and its participation in neurodegeneration. We conclude that sEH is one of the most promising therapeutic strategies for PD and other neurodegenerative diseases with chronic inflammation process, providing new insights into the crucial role of sEH in PD pathophysiology as well as a singular opportunity for drug development

Modeling tourists' personality in recommender systems: how does personality influence preferences for tourist attractions?

Personalization is increasingly being perceived as an important factor for the effectiveness of Recommender Systems (RS). This is especially true in the tourism domain, where travelling comprises emotionally charged experiences, and therefore, the more about the tourist is known, better recommendations can be made. The inclusion of psychological aspects to generate recommendations, such as personality, is a growing trend in RS and they are being studied to provide more personalized approaches. However, although many studies on the psychology of tourism exist, studies on the prediction of tourist preferences based on their personality are limited. Therefore, we undertook a large-scale study in order to determine how the Big Five personality dimensions influence tourists' preferences for tourist attractions, gathering data from an online questionnaire, sent to Portuguese individuals from the academic sector and their respective relatives/friends (n=508). Using Exploratory and Confirmatory Factor Analysis, we extracted 11 main categories of tourist attractions and analyzed which personality dimensions were predictors (or not) of preferences for those tourist attractions. As a result, we propose the first model that relates the five personality dimensions with preferences for tourist attractions, which intends to offer a base for researchers of RS for tourism to automatically model tourist preferences based on their personality.GrouPlanner Project under the European Regional Development Fund POCI-01-0145-FEDER29178 and by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within the Projects UIDB/00319/2020 and UIDB/00760/202

Role and task recommendation and social tagging to enable social business process management

Traditional Business Process Management (BPM) poses a number of limitations for the management of ad-hoc processes, where the execution paths are not designed a priori and evolve during enactment. Social BPM, which predicates to integrate social software into the BPM lifecycle, has emerged as an answer to such limitations. This paper presents a framework for social BPM in which social tagging is used to capture process knowledge emerging during the enactment and design of the processes. Process knowledge concerns both the type of activities chosen to fulfil a certain goal and the skills and experience of users in executing specific tasks. Such knowledge is exploited by recommendation tools to support the design and enactment of future process instances. We first provide an overview of our framework, introducing the concepts of role and task recommendations, which are supported by social tagging. These mechanisms are then elaborated further by an example. Eventually, we discuss a prototype of our framework enabling collaborative process design and execution

A personalized and context-aware news offer for mobile devices

For classical domains, such as movies, recommender systems have proven their usefulness. But recommending news is more challenging due to the short life span of news content and the demand for up-to-date recommendations. This paper presents a news recommendation service with a content-based algorithm that uses features of a search engine for content processing and indexing, and a collaborative filtering algorithm for serendipity. The extension towards a context-aware algorithm is made to assess the information value of context in a mobile environment through a user study. Analyzing interaction behavior and feedback of users on three recommendation approaches shows that interaction with the content is crucial input for user modeling. Context-aware recommendations using time and device type as context data outperform traditional recommendations with an accuracy gain dependent on the contextual situation. These findings demonstrate that the user experience of news services can be improved by a personalized context-aware news offer

Alleviating the new user problem in collaborative filtering by exploiting personality information

The final publication is available at Springer via http://dx.doi.org/10.1007/s11257-016-9172-zThe new user problem in recommender systems is still challenging, and there is not yet a unique solution that can be applied in any domain or situation. In this paper we analyze viable solutions to the new user problem in collaborative filtering (CF) that are based on the exploitation of user personality information: (a) personality-based CF, which directly improves the recommendation prediction model by incorporating user personality information, (b) personality-based active learning, which utilizes personality information for identifying additional useful preference data in the target recommendation domain to be elicited from the user, and (c) personality-based cross-domain recommendation, which exploits personality information to better use user preference data from auxiliary domains which can be used to compensate the lack of user preference data in the target domain. We benchmark the effectiveness of these methods on large datasets that span several domains, namely movies, music and books. Our results show that personality-aware methods achieve performance improvements that range from 6 to 94 % for users completely new to the system, while increasing the novelty of the recommended items by 3-40 % with respect to the non-personalized popularity baseline. We also discuss the limitations of our approach and the situations in which the proposed methods can be better applied, hence providing guidelines for researchers and practitioners in the field.This work was supported by the Spanish Ministry of Economy and Competitiveness (TIN2013-47090-C3). We thank Michal Kosinski and David Stillwell for their attention regarding the dataset

The Semantics of Movie Metadata: Enhancing User Profiling for Hybrid Recommendation

In movie/TV collaborative recommendation approaches, ratings users gave to already visited content are often used as the only input to build profiles. However, users might have rated equally the same movie but due to different reasons: either because of its genre, the crew or the director. In such cases, this rating is insufficient to represent in detail users’ preferences and it is wrong to conclude that they share similar tastes. The work presented in this paper tries to solve this ambiguity by exploiting hidden semantics in metadata elements. The influence of each of the standard description elements (actors, directors and genre) in representing user’s preferences is analyzed. Simulations were conducted using Movielens and Netflix datasets and different evaluation metrics were considered. The results demonstrate that the implemented approach yields significant advantages both in terms of improving performance, as well as in dealing with common limitations of standard collaborative algorithm.info:eu-repo/semantics/publishedVersio